code: def test_estimate_cpd_shortcuts(self):
      cpd_C1 = self.est2.estimate_cpd(
          "C", prior_type="BDeu", equivalent_sample_size=9
      )
      cpd_C1_correct = TabularCPD(
          "C",
          3,
          [
              [0.2, 0.2, 0.6, 1.0 / 3, 1.0 / 3, 1.0 / 3],
              [0.6, 0.6, 0.2, 1.0 / 3, 1.0 / 3, 1.0 / 3],
              [0.2, 0.2, 0.2, 1.0 / 3, 1.0 / 3, 1.0 / 3],
          ],
          evidence=["A", "B"],
          evidence_card=[3, 2],
          state_names={"A": [0, 1, 2], "B": [0, 1], "C": [0, 1, 23]},
      )
      self.assertEqual(cpd_C1, cpd_C1_correct)
  
      cpd_C2 = self.est3.estimate_cpd("C", prior_type="K2")
      cpd_C2_correct = TabularCPD(
          "C",
          2,
          [
              [0.5, 0.6, 1.0 / 3, 2.0 / 3, 0.75, 2.0 / 3],
              [0.5, 0.4, 2.0 / 3, 1.0 / 3, 0.25, 1.0 / 3],
          ],
          evidence=["A", "B"],
          evidence_card=[3, 2],
          state_names={"A": [0, 1, 2], "B": ["X", "Y"], "C": [0, 1]},
      )
      self.assertEqual(cpd_C2, cpd_C2_correct)
  
imports: import unittest
  import numpy
  import pandas
  from joblib.externals.loky import get_reusable_executor
  from pgmpy import config
  from pgmpy.estimators import BayesianEstimator
  from pgmpy.factors.discrete import TabularCPD
  from pgmpy.models import BayesianNetwork
label_desc: Use image node_id 5 for calling the TestBayesianEstimatorTorch obj's underlying member method code with example usage: obj.test_estimate_cpd_shortcuts() without return types